import gradio as gr
import numpy as np
from PIL import Image
import requests

import hopsworks
import joblib

project = hopsworks.login()
fs = project.get_feature_store()


mr = project.get_model_registry()
model = mr.get_model("titanic_modal", version=1)
model_dir = model.download()
model = joblib.load(model_dir + "/titanic_model.pkl")

# "Pclass", "Sex", "Age", "Parch"
def titanic(pclass, sex, age, parch):
    input_list = []
    input_list.append(pclass)
    input_list.append(sex)
    input_list.append(age)
    input_list.append(parch)
    # 'res' is a list of predictions returned as the label.
    res = model.predict(np.asarray(input_list).reshape(1, -1)) 
    # We add '[0]' to the result of the transformed 'res', because 'res' is a list, and we only want 
    # the first element.
    if res[0] == 0:
        titanic_url = "https://lh3.googleusercontent.com/drive-viewer/AFDK6gNF4xxysDnR9rP3VtnQ0gqQABJaF9MHN6fx9ndnGEp3HwuAH8X4Qh-ewYGg-DbgtNM0q3mKfBKVNK2wyvZdPkGvnKpE=w1920-h487"
    elif res[0] == 1:
        titanic_url = "https://lh3.googleusercontent.com/fife/AAbDypAIrE-Me7Sqj3LvMGiOSqrypr9JZfdlWp46clUTEYaZC6zzZeF0iT2zi49Hrj8B0-I4DNb7ce1bqCAFDbNMhQnPNhtjXmrgfV-aFuGpUc6otrmuBT8mM6xpeyW5ljhTcLKTg2YUaY6E2d_XCj8WHY3BTXJvbU55KyOplRicpcLpElmXW2s4RW9op-3waE4p6uY0fIRphNMfxjCZDpCzlmni-zq3RsT3YL8CZaV_03CfW84ntB4nirFedH6zfuWhgreTdKmw6lqoHWm9QgRji33fJJ4bG9APfQCB2anF9z7GJJ3_pcHr39e4qtwxeKIta8sFUsXPKZE7SEHPNdfO_yGV34vMDxsfBUgbRLNOzw06HAVQuvUk1Oe4DSd1GyZxNWsSzw1D8DZhvZzHxHlFcc27d_qglMEtNQGVhulZFb2A3ZGR9I2Us7jog2UVnpjyGTLBlMUX_-K1WpUSbsoq9aHGb6rOWN4ZyZ5r2c_yJU4NzFNmQDZInKBsZKEK0S7oAm69VQ8RVZ6O_3vgy-7K-r-3pEPnTuDVFEndSwDJvYOulagpCEy0i-LhdToN0C2Va6wBAAocXHgsif9PcuR7IAmql-gk06iBGt1YtveOBNfAakoTqvD-BGPHLARGosKoS0csT_AJENIV3wIgEYr7E6plpNBvUSjpQGvGF3Bv1WUPQxZkEof4VV1loqX1vDHQYo4IPpbLBhyPnK_Ate09BPkDc4qEmqhgREEddDQbxzYF2yLz_U-wt_IGbJfUlBikFK6QA8emZUG06yc3vEl4y7bhA1Ofy4eEySMqKhCsKdNQfwzydTUUhvG3U5D70dXXhF0rZy6HfwuIJ-wneawSyvKScEyHBH07vE1YYoMKZO8HmAYboDbaHg5Vyy4cEH8Q4ORi8_9WDxPaGbmsXlqmeEtpplm6CKCq8Qsbc7vKelIzqe7rxZfffBbEjKrA8pp2eBD21Z9zJu5MmitKgj3DPWKxMsCLyuKz02ple_NT8IIpliGP7IUJ2W30x5xEmuCB8-winTszhSGOY_49tywV4x8oumgGeT6qzqrde1qlZkeOX_I5SxHgP56o3ApiI_sDSjaWOHyTmY3FQfBAq9_zTCosvS9113S2mXdeSSGYPTW_8303TqoXEYrGiEwwRitA_-0r-BgxfbPCk7muZfqpr5sgnjoDsTmcgxHXFWVr91IASGIz_8Igl5llof951CndkvDrJFuHycKNxjifzBqe-41Ivw-v-IJ8lpB28yiIjw6rtoiTflGOnUK-dnrbanykYus3vhitOZ_r1JCUWK9nU6o4WxcHzJONVyhG3Nt_-vk_EHncI_bWjTfKE8FUDH94QuO3bU6R1T-5sJKmueOGpIG-4jlMx1V4LM6ylel3Dv6k_dh9Ah7WCs6IsMRQ-_7lWOt9dCNntEdcm2hc9jRrrBxgMSB-6IJ5c90MXjj-yiYisGH3K6keppoR9iaddtKWkyo3d0VEobsTPRUyMdQ=w1920-h892"
    img = Image.open(requests.get(titanic_url, stream=True).raw)            
    return img
        
demo = gr.Interface(
    fn=titanic,
    title="Titanic Passenger Survival Predictive Analytics",
    description="Experiment to predict passenger survival on the Titanic.",
    allow_flagging="never",
    inputs=[
        gr.inputs.Number(default=1.0, label="Pclass (1st class -> 3 class)"),
        gr.inputs.Number(default=1.0, label="Sex (0 = Female, 1 = Male)"),
        gr.inputs.Number(default=1.0, label="Age (0 -> 100 years)"),
        gr.inputs.Number(default=1.0, label="Parch (0 -> 2 parents/children on board)"),
        ],
    outputs=gr.Image(type="pil"))

demo.launch()